Speech-language pathologists’ assessment and intervention practices with multilingual children
Why this work is in the frame
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Bibliographic record
Abstract
Within predominantly English-speaking countries such as the US, UK, Canada, New Zealand, and Australia, there are a significant number of people who speak languages other than English. This study aimed to examine Australian speech-language pathologists' (SLPs) perspectives and experiences of multilingualism, including their assessment and intervention practices, and service delivery methods when working with children who speak languages other than English. A questionnaire was completed by 128 SLPs who attended an SLP seminar about cultural and linguistic diversity. Approximately one half of the SLPs (48.4%) reported that they had at least minimal competence in a language(s) other than English; but only 12 (9.4%) reported that they were proficient in another language. The SLPs spoke a total of 28 languages other than English, the most common being French, Italian, German, Spanish, Mandarin, and Auslan (Australian sign language). Participants reported that they had, in the past 12 months, worked with a mean of 59.2 (range 1-100) children from multilingual backgrounds. These children were reported to speak between two and five languages each; the most common being: Vietnamese, Arabic, Cantonese, Mandarin, Australian Indigenous languages, Tagalog, Greek, and other Chinese languages. There was limited overlap between the languages spoken by the SLPs and the children on the SLPs' caseloads. Many of the SLPs assessed children's speech (50.5%) and/or language (34.2%) without assistance from others (including interpreters). English was the primary language used during assessments and intervention. The majority of SLPs always used informal speech (76.7%) and language (78.2%) assessments and, if standardized tests were used, typically they were in English. The SLPs sought additional information about the children's languages and cultural backgrounds, but indicated that they had limited resources to discriminate between speech and language difference vs disorder.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it